A systems approach to assessing cropping systems on the Canadian Prairies: yield and economic returns
Bibliographic record
Abstract
Agriculture faces grand challenges of improving productivity and profitability under climate uncertainty. A systems approach is required when designing crop systems to achieve the long-term goal of sustainability. A 4-year crop rotation study was established in 2018 at seven sites across the Canadian Prairies, including Beaverlodge, Lacombe, and Lethbridge, AB; Melfort, Scott, and Swift Current, SK; and Carman, MB. The overall objective of this project is to develop resilient cropping systems in different ecozones on the Canadian Prairies. This study tests six cropping systems consisting of 1) conventional cropping system (Control), 2) pulse- or oilseed-intensified cropping system (POS), 3) diversified cropping system (DS), 4) market-driven cropping system (MS), 5) high risk and high reward cropping system (HRHRS), and 6) green-manure incorporated soil-health focused cropping system (GMS). At the end of the 4-yr rotations, we will assess the performance of cropping systems using a suit of indicators including productivity, soil health, resource use efficiency, pest incidence, economic returns, environmental impacts, and resilience. The first 3-year preliminary results indicated that the average system yield (e.g. canola equivalent yield) for MS (2167 kg ha-1) and POS (1758 kg ha-1) were 33 and 8% higher than Control (1632 kg ha-1), respectively; while the CEY for DS (1537 kg ha-1), HRHRS (1451 kg ha-1) and GMS (1319 kg ha-1) were 6, 11, and 19% lower than Control, respectively. The net returns follow the order of MS > POS > DS > Control > GMS > HRHRS. Yield stability follows the order of GMS > POS > Control > DS > MS > HRHRS. The preliminary results suggest that an integrated approach is required to develop and assess cropping systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".